OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool Nilesh Patil 1, a,*, Atul Saraf 2, b, Atul Kulkarni 3, c 1 Marathwada Institute of Technology, Aurangabad-431010, Maharashtra State, India 2 National Institute of Technology, Surat, Gujarat 395007, India 3 Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune – 411048, Maharashtra, India a https://orcid.org/0000-0002-4884-4267, nileshgpatil@rediff mail.com; b https://orcid.org/0000-0003-4776-6874, atul.saraf001@gmail.com; c https://orcid.org/0000-0002-6452-6349, atul.kulkarni@viit.ac.in Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2024 vol. 26 no. 1 pp. 155–174 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2024-26.1-155-174 ART I CLE I NFO Article history: Received: 20 September 2023 Revised: 31 October 2023 Accepted: 22 January 2024 Available online: 15 March 2024 Keywords: Semi-empirical model Regression model Temperature Surface roughness For citation: Patil N.G., Saraf A.R., Kulkarni A.P Semi empirical modeling of cutting temperature and surface roughness in turning of engineering materials with TiAlN coated carbide tool. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2024, vol. 26, no. 1, pp. 155–174. DOI: 10.17212/1994-6309-2024-26.1-155-174. (In Russian). ______ * Corresponding author Kulkarni Atul P., Ph.D. (Engineering), Professor Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk), Pune – 411048, Maharashtra, India Tel.: 91-2026950419, e-mail: atul.kulkarni@viit.ac.in Introduction. In manufacturing, obtaining a given surface roughness of the machined parts is of great importance to fulfi ll functional requirements. However, the surface roughness signifi cantly aff ected by the heat generated during the machining process, which can lead to a decrease in dimensional accuracy. The surface roughness signifi cantly aff ects the fatigue characteristics of the part, and the service life of the cutting tool is determined by the cutting temperature generation. The purpose of the work. The purpose of this study is to create semi-empirical models for predicting surface roughness and temperature of various work materials. Enhanced cutting performance is achieved by accurately determining the cutting temperature in the machined zone. However, calculating the cutting temperature for each specifi c case is fraught with diffi culties in terms of labor resources and fi nancial investments. This paper presents a comprehensive empirical formula designed to predict both theoretical temperature and surface roughness. Methodology, The performance of the surface roughness and temperature generation was evaluated for the EN 8, Al 380, SS 316 and SAE 8620 materials when processed with TiAlN-coated carbide tools. The TiAlN coating was obtained by Physical Vapor Deposition (PVD) technique. Response surface methodology was used to prepare predictive models. Cutting speed (from 140 to 340 m/min), feed (from 0.08 to 0.24 mm/rev) and depth of cut (from 0.6 to 1 mm) were used as input parameters to measure the characteristics of all materials in terms of surface roughness and cutting temperature. The tool-work thermocouple principle was used to measure the temperature at the chip-tool interface. Novel Calibration Setup was developed to establish the relationship between the Electromotive Force (EMF) generated during machining and the cutting temperature. Results and Discussion. It is observed that the energy required for mechanical processing was largely converted into heat. The highest cutting temperature is recorded with SS 316, followed by SAE 8620 and EN 8. However, low temperature was reported during machining of Al 380 and it was mainly governed by the thermal conductivity of the material. The lowest surface roughness is observed for SAE 8620, EN 8, followed by SS 316 and Al 380. The semi-empirical method and regression model equations are in good agreement with each other. Statistical analysis of the nonlinear evaluation reveals that cutting speed, feed rate, and material density have a greater infl uence on the surface roughness, whereas depth of cut has a greater infl uence on the temperature change. The study will be very useful for predicting industrial performance when machining EN 8, Al 380, SS 316 and SAE 8620 materials with TiAlN-coated carbide tools.
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